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Nazanin Nezami; Parian Haghighat; Denisa Gándara; Hadis Anahideh – Grantee Submission, 2024
The education sector has been quick to recognize the power of predictive analytics to enhance student success rates. However, there are challenges to widespread adoption, including the lack of accessibility and the potential perpetuation of inequalities. These challenges present in different stages of modeling, including data preparation, model…
Descriptors: Evaluation Methods, College Students, Success, Predictor Variables
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Kosei Fukuda – Teaching Statistics: An International Journal for Teachers, 2024
In statistics classes, the central limit theorem has been demonstrated using simulation-based illustrations. Known population distributions such as a uniform or exponential distribution are often used to consider the behavior of the sample mean in simulated samples. Unlike such simulations, a number of real-data-based simulations are here…
Descriptors: Foreign Countries, Business, Business Administration Education, Sample Size
Cody Gene Singer – ProQuest LLC, 2023
College and university enrollment has decreased nationwide every year for more than a decade as educational consumers increasingly question the value of higher education and discover alternatives to the traditional university system. Enrollment professionals seeking growth are tasked to develop and implement innovative solutions to address…
Descriptors: Data Collection, Predictor Variables, Electronic Learning, Enrollment
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Phillips, Tanner M.; Saleh, Asmalina; Ozogul, Gamze – International Journal of Artificial Intelligence in Education, 2023
Encouraging teachers to reflect on their instructional practices and course design has been shown to be an effective means of improving instruction and student learning. However, the process of encouraging reflection is difficult; reflection requires quality data, thoughtful analysis, and contextualized interpretation. Because of this, research on…
Descriptors: Reflection, Artificial Intelligence, Natural Language Processing, Data Collection
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Dina Fitria Murad; Meta Amalya Dewi; Arbaiah Inn; Silvia Ayunda Murad; Noor Udin; Taufik Darwis – Journal of Educators Online, 2025
This study aims to produce a more personalized recommendation system for online learning using multicriteria in collaborative filtering and data from the Binus Online Learning repository as a knowledge base. The study uses forecasting (regression) and consists of three stages: (1) collecting data on the results of the learning process; (2) adding…
Descriptors: Electronic Learning, Data Collection, Context Effect, Learning Processes
Hoffman, Nancy; O'Connor, Anna; Mawhinney, Joanna – Jobs for the Future, 2022
The purpose of this brief is to provide school-level examples of how early college practitioners are collecting and using data to improve their practices. Examples three and four are school-level data from two early college partnerships: the MetroWest CPC (Framingham, Milford, Waltham), and Lawrence. The brief begins, however, with the national…
Descriptors: College School Cooperation, Partnerships in Education, High Schools, Universities
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Faucon, Louis; Olsen, Jennifer K.; Haklev, Stian; Dillenbourg, Pierre – Journal of Learning Analytics, 2020
In classrooms, some transitions between activities impose (quasi-)synchronicity, meaning there is a need for learners to move between activities at the same time. To make real-time decisions about when to move to the next activity, teachers need to be able to balance the progress of their students as they work at different paces. In this paper, we…
Descriptors: Classroom Techniques, Prediction, Learning Activities, Student Behavior
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Ali, Amira D.; Hanna, Wael K. – Journal of Educational Computing Research, 2022
With the spread of the COVID-19 pandemic, many universities adopted a hybrid learning model as a substitute for a traditional one. Predicting students' performance in hybrid environments is a complex task because it depends on extracting and analyzing different types of data: log data, self-reports, and face-to-face interactions. Students must…
Descriptors: Predictor Variables, Academic Achievement, Blended Learning, Independent Study
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Cannistrà, Marta; Masci, Chiara; Ieva, Francesca; Agasisti, Tommaso; Paganoni, Anna Maria – Studies in Higher Education, 2022
This paper combines a theoretical-based model with a data-driven approach to develop an Early Warning System that detects students who are more likely to dropout. The model uses innovative multilevel statistical and machine learning methods. The paper demonstrates the validity of the approach by applying it to administrative data from a leading…
Descriptors: Dropouts, Potential Dropouts, Dropout Prevention, Dropout Characteristics
Glazer, Randy – ProQuest LLC, 2019
Employee turnover continues to be discussed as an outcome in Human Resources (HR), but comparatively few studies have examined the relationship between turnover as the independent variable and institutional outcomes. Although the call to HR practitioners has often been made over the past 20 years regarding the importance of tying HR programs and…
Descriptors: Labor Turnover, Employees, Correlation, Human Resources
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Weiand, Augusto; Manssour, Isabel Harb; Silveira, Milene Selbach – International Journal of Distance Education Technologies, 2019
With technological advances, distance education has been frequently discussed in recent years. The learning environments used in this course usually generates a great deal of data because of the large number of students and the various tasks involving their interaction. In order to facilitate the analysis of the data, the authors researched to…
Descriptors: Foreign Countries, Distance Education, Online Courses, Visualization
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Morsomme, Raphaël; Alferez, Sofia Vazquez – International Educational Data Mining Society, 2019
Liberal Arts programs are often characterized by their open curriculum. Yet, the abundance of courses available and the highly personalized curriculum are often overwhelming for students who must select courses relevant to their academic interests and suitable to their academic background. This paper presents the course recommender system that we…
Descriptors: Liberal Arts, Course Selection (Students), Courses, College Students
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Chaudhary, Richa – Education & Training, 2017
Purpose: The purpose of this paper is to investigate the influence of demographic, social and personal dispositional factors on determining the entrepreneurial inclination. Specifically, the author examined the role of gender, age, stream of study, family business background and six psychological traits of locus of control, tolerance for…
Descriptors: Entrepreneurship, Demography, Foreign Countries, Personality Traits
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Barros, Thiago M.; Souza Neto, Plácido A.; Silva, Ivanovitch; Guedes, Luiz Affonso – Education Sciences, 2019
Predicting school dropout rates is an important issue for the smooth execution of an educational system. This problem is solved by classifying students into two classes using educational activities related statistical datasets. One of the classes must identify the students who have the tendency to persist. The other class must identify the…
Descriptors: Predictor Variables, Models, Dropout Rate, Classification
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Montgomery, Amanda P.; Mousavi, Amin; Carbonaro, Michael; Hayward, Denyse V.; Dunn, William – British Journal of Educational Technology, 2019
Blended learning (BL) is a popular e-Learning model in higher education that has the potential to take advantage of learning analytics (LA) to support student learning. This study utilized LA to investigate fourth-year undergraduates' (n = 157) use of self-regulated learning (SRL) within the online components of a previously unexamined BL…
Descriptors: Blended Learning, Educational Technology, Higher Education, Undergraduate Students
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